Akt Activates the Mammalian Target of Rapamycin by Regulating Cellular ATP Level and AMPK Activity
Bibliographic record
Abstract
The serine/threonine kinase Akt is an upstream positive regulator of the mammalian target of rapamycin (mTOR). However, the mechanism by which Akt activates mTOR is not fully understood. The known pathway by which Akt activates mTOR is via direct phosphorylation and inhibition of tuberous sclerosis complex 2 (TSC2), which is a negative regulator of mTOR. Here we establish an additional pathway by which Akt inhibits TSC2 and activates mTOR. We provide for the first time genetic evidence that Akt regulates intracellular ATP level and demonstrate that Akt is a negative regulator of the AMP-activated protein kinase (AMPK), which is an activator of TSC2. We show that in Akt1/Akt2 DKO cells AMP/ATP ratio is markedly elevated with concomitant increase in AMPK activity, whereas in cells expressing activated Akt there is a dramatic decrease in AMP/ATP ratio and a decline in AMPK activity. Currently, the Akt-mediated phosphorylation of TSC2 and the inhibition of AMPK-mediated phosphorylation of TSC2 are viewed as two separate pathways, which activate mTOR. Our results demonstrate that Akt lies upstream of these two pathways and induces full inhibition of TSC2 and activation of mTOR both through direct phosphorylation and by inhibition of AMPK-mediated phosphorylation of TSC2. We propose that the activation of mTOR by Akt-mediated cellular energy and inhibition of AMPK is the predominant pathway by which Akt activates mTOR in vivo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".